{Conditional mixed-state model for structural change analysis from very high resolution optical images},

year

=

{2009},

month

=

{July},

booktitle

=

{IGARSS},

address

=

{Cape Town, South Africa},

url

=

{http://hal.archives-ouvertes.fr/inria-00398062/},

keyword

=

{Change detection, mixed Markov models}

}

Abstract :

The present work concerns the analysis of dynamic scenes from earth observation images. We are interested in building a map which, on one hand locates places of change, on the other hand, reconstructs a unique visual information of the non-change areas. We show in this paper that such a problem can naturally be takled with conditional mixed-state random field modeling (mixed-state CRF), where the ”mixed state” refers to the symbolic or continous nature of the unknown variable. The maximum a posteriori (MAP) estimation of the CRF is, through the Hammersley-Clifford theorem, turned into an energy minimisation problem. We tested the model on several Quickbird images and illustrate the quality of the results.